We have also been developing computational methods for defining signatures of oncogene activity and essentiality. In efforts led by Pablo Tamayo and Jill Mesirov (Broad), we have begun testing new prediction approaches using rank-based metrics that are aimed at being microarray platform-independent. Working in collaboration with Alejandro Sweet-Cordero (Stanford) and Tyler Jacks (MIT), we have focused on signatures of KRAS as an example, and have focused on developing approaches to integrating experimental KRAS manipulation (gain and loss of function) with primary patient sample data (for which KRAS mutation status is known). We have particularly focused on trying to better understand the basis for context (cell type) dependent predictors of KRAS activation status, given our observation that while there may be a 'generic' gne expression signature of KRAS activation, the robustness of that signature is low compared to those that can be developed within a given cell type.